PMLE Practice Test 20 — 15 Questions

Question 1

Drag and drop the steps to deploy a trained TensorFlow model to Vertex AI Prediction in the correct order.

Accepted Answer

Export the model, then upload to GCS, then register as a model, then deploy to endpoint, then test. Export the model, upload to GCS, register as a model, deploy to endpoint, then test.

Answer

Upload to GCS, then export the model, then register as a model, then deploy to endpoint, then test

Answer

Export the model, then register as a model, then upload to GCS, then deploy to endpoint, then test

Answer

Export the model, then upload to GCS, then deploy to endpoint, then register as a model, then test

Question 2

An ML engineer needs to monitor the error rate of prediction jobs on a Vertex AI Endpoint. Where can they view the number of failed prediction requests over time?

Accepted Answer

Cloud Monitoring. Cloud Monitoring (formerly Stackdriver) is the Google Cloud service that collects, stores, and visualizes metrics such as prediction request counts and error rates for Vertex AI Endpoints. It provides dashboards, alerting, and time-series charts for failed prediction requests over time. This is the correct place to monitor error rate metrics.

Answer

Cloud Console endpoint details page

Answer

Vertex AI Experiments

Answer

Cloud Logging

Question 3

A team of data scientists and ML engineers is collaborating on a shared feature store in Vertex AI Feature Store. They need to ensure that feature definitions are versioned and that changes are reviewed before being used in production pipelines. Which TWO practices should they implement?

Accepted Answer

Require code reviews for all changes to feature definitions before merging to the main branch.. Requiring code reviews for all changes to feature definitions before merging to the main branch enforces a peer-review gate, ensuring that modifications are validated for correctness, consistency, and compliance before they reach production. This aligns with MLOps best practices for governance and reduces the risk of introducing errors or breaking changes into the feature store.

Answer

Allow data scientists to edit feature definitions directly in the Vertex AI Feature Store console.

Answer

Define multiple feature views in Vertex AI Feature Store for different environments and manage access via IAM.

Answer

Use scheduled batch jobs to synchronize feature definitions from a shared spreadsheet to Vertex AI Feature Store.

Question 4

An ML team wants to implement data versioning for large datasets stored in Google Cloud Storage. They need to track changes over time and reproduce previous data states. Which tool is most appropriate?

Accepted Answer

DVC. DVC (Data Version Control) is purpose-built for versioning large datasets and ML models alongside Git. It stores small metadata files in Git while pushing the actual large data to remote storage like Google Cloud Storage, enabling teams to track dataset changes over time and reproduce exact previous data states via commits or tags.

Answer

Cloud Storage Object Versioning

Answer

BigQuery table snapshots

Answer

Git LFS

Question 5

An organization needs to implement MLOps with standardized pipeline templates across multiple teams. Which Vertex AI feature should they use to create reusable pipeline components?

Accepted Answer

Vertex AI Pipelines. Vertex AI Pipelines is the managed orchestration service that lets teams define, run, and monitor ML workflows as directed acyclic graphs (DAGs) using Kubeflow Pipelines or TFX. It supports reusable pipeline components and templates, enabling standardization across teams by packaging preprocessing, training, evaluation, and deployment steps into versioned, shareable artifacts.

Answer

Vertex AI Experiments

Answer

Vertex AI Metadata

Answer

Vertex AI Workbench

Question 6

A company uses Vertex AI Feature Store with an online store for low-latency serving. They observe high latency during peak hours. The feature values are small (< 1 KB each) and the workload is read-heavy. Which change would most effectively reduce latency?

Accepted Answer

Switch from Bigtable online store to Optimized online store. Switching from Bigtable online store to Optimized online store is recommended for read-heavy workloads with small feature values, offering lower latency at high QPS.

Answer

Enable caching on the client side

Answer

Use a larger machine type for Bigtable

Answer

Increase the number of Bigtable nodes

Question 7

An ML engineer is training a very large PyTorch model on Vertex AI using a TPU v3 pod. The training is slower than expected, and the TPU utilization is low. What is the most likely cause?

Accepted Answer

The data pipeline is a bottleneck; the TPU is waiting for data.. The most likely cause of low TPU utilization is a data pipeline bottleneck, where the TPU spends a significant amount of time idle waiting for the next batch of data to be loaded and preprocessed. TPU v3 pods are designed for high-throughput matrix operations and can process data far faster than a typical CPU-based data loader can supply it, especially if the data pipeline uses inefficient I/O, lacks prefetching, or has insufficient workers. This mismatch starves the TPU, leading to low utilization and slower training.

Answer

The learning rate schedule is too aggressive.

Answer

The model is using a single TensorFlow operation not supported by TPU.

Answer

The batch size is too large for the TPU memory.

Question 8

You are fine-tuning a pre-trained BERT model from Hugging Face on a custom text classification dataset using Vertex AI Training. You want to speed up training by using mixed precision. What should you do?

Accepted Answer

Set fp16=True in the TrainingArguments. Hugging Face's Trainer API exposes mixed precision through the TrainingArguments parameter fp16=True (or bf16=True for bfloat16). Setting this flag enables automatic mixed precision (AMP) via PyTorch's torch.cuda.amp, which casts eligible operations to FP16 while keeping master weights in FP32 for numerical stability. This is the standard, supported way to accelerate fine-tuning on GPU without rewriting the model.

Answer

Modify the model to use half-precision layers

Answer

Use a custom container with TensorFlow instead of PyTorch

Answer

Enable mixed precision via Vertex AI hyperparameter tuning

Question 9

A team is training a large TensorFlow model that requires more memory than a single GPU provides. They have access to multiple GPUs on a single machine. Which distributed training strategy should they use to split the model layers across GPUs?

Accepted Answer

Manual device placement using tf.device to assign layers to specific GPUs. When a single model's layers exceed one GPU's memory, the model itself must be partitioned across devices — this is model parallelism. Manual device placement with tf.device('/GPU:0'), tf.device('/GPU:1'), etc. is the TensorFlow-native way to assign specific layers or operations to specific GPUs, splitting the model across them.

Answer

tf.distribute.experimental.MultiWorkerMirroredStrategy

Answer

tf.distribute.experimental.ParameterServerStrategy

Answer

tf.distribute.MirroredStrategy

Question 10

A company trains a model using features from Vertex AI Feature Store. They notice training-serving skew because the feature values used at training time differ from those served online. How should they address this?

Accepted Answer

Use point-in-time correct retrieval from the offline store for training data. Training-serving skew occurs when the feature values used during training differ from those served at inference time. Point-in-time correct retrieval from the offline store ensures that, for each training example, the feature values used are exactly those that would have been available at that timestamp — matching what the online store would have served. This eliminates the temporal mismatch that causes skew.

Answer

Use the same online store for both training and serving

Answer

Disable caching in the online store

Answer

Enable feature monitoring to detect drift

Question 11

You want to use Vertex AI JumpStart to quickly deploy a pre-built foundation model for text summarization. Which action is required?

Accepted Answer

Select the model from Model Garden and deploy it to a Vertex AI endpoint. Vertex AI JumpStart provides pre-built foundation models in Model Garden that can be deployed directly to a Vertex AI endpoint with minimal configuration. Selecting the model from Model Garden and deploying it to an endpoint is the standard JumpStart workflow for getting a foundation model into production for tasks like text summarization. No training, containerization, or batch export is required because JumpStart handles the deployment plumbing.

Answer

Train the model from scratch using Vertex AI Training

Answer

Export the model to a Cloud Storage bucket and use batch prediction

Answer

Build a custom Docker container with the model and deploy to Vertex AI

Question 12

An ML engineer is configuring Vertex AI Model Monitoring for drift detection on a deployed endpoint. Which TWO settings directly affect the frequency and accuracy of drift detection? (Choose 2)

Accepted Answer

Sampling rate. Sampling rate controls what fraction of predictions is analyzed; monitoring frequency controls how often the distribution comparison is performed. Both directly impact detection speed and accuracy.

Answer

Model version

Answer

Explanation method

Answer

Alerting threshold

Question 13

Your Vertex AI custom training job is failing with an out-of-memory error on a single GPU. You need to reduce memory usage without changing the model architecture. Which approach should you try first?

Accepted Answer

Decrease the batch size. Decreasing the batch size directly reduces the memory required for activations and gradients, making it the simplest and most immediate way to resolve out-of-memory errors without altering the model architecture. It is a standard first step because it requires no code changes beyond a hyperparameter and often resolves OOM with minimal impact on convergence if adjusted with learning rate.

Answer

Implement model parallelism across GPUs

Answer

Use gradient accumulation

Answer

Enable mixed precision training (FP16)

Question 14

A company wants to automatically retrain their model when data drift is detected. Which THREE components are needed to implement this pipeline?

Accepted Answer

Cloud Function to invoke Vertex AI Pipeline. Option A (Cloud Function to invoke Vertex AI Pipeline) is correct because the Cloud Function acts as the automation trigger that programmatically starts the Vertex AI Pipeline run to retrain the model once drift is signaled. Option C (Cloud Monitoring alert policy for drift metric) is correct because Vertex AI Model Monitoring publishes drift metrics to Cloud Monitoring, and an alerting policy on that metric is what detects the drift condition and fires the event. Option D (Pub/Sub topic) is correct because the alert policy notification channel uses Pub/Sub to deliver the drift alert, which then triggers the Cloud Function, forming the event-driven chain (Monitoring alert → Pub/Sub → Cloud Function → Vertex AI Pipeline). Option B (Vertex AI Feature Store) is not required here since it is for serving/online feature management, not for the drift-detection-and-retrain trigger mechanism. Option E (Cloud Storage bucket for storing training data) is not required as a distinct component of this pipeline, since training data storage is already assumed to exist and is not part of the drift-triggered automation chain.

Answer

Vertex AI Feature Store

Answer

Cloud Storage bucket for storing training data

Question 15

A company is migrating from an on-premises ML serving infrastructure to Vertex AI. They have multiple models that need to be served from the same endpoint with different traffic percentages. They also need to monitor prediction quality. Which THREE actions should they take? (Choose 3)

Accepted Answer

Deploy multiple model versions on the same endpoint with traffic_split parameter.. Option A is correct because Vertex AI endpoints natively support serving multiple DeployedModels behind a single endpoint, and the traffic_split parameter on the endpoint lets you route configurable percentages of prediction traffic to each model version. Option C is correct because Vertex AI Model Monitoring is the managed service that detects training-serving skew and prediction drift on deployed models, directly addressing the requirement to monitor prediction quality. Option D is correct because prediction quality often depends on business outcomes that Model Monitoring does not capture, so Cloud Monitoring custom metrics let you track those outcome-based signals alongside Vertex AI metrics. Option B is not appropriate because separate endpoints with Cloud Load Balancing adds unnecessary complexity and does not provide Vertex AI's built-in traffic splitting or model monitoring integration. Option E is insufficient because exporting logs to BigQuery for manual analysis only is not an automated monitoring solution and does not satisfy the prediction-quality monitoring requirement.

Answer

Deploy each model as a separate endpoint and use Cloud Load Balancing.

Answer

Export logs to BigQuery for manual analysis only.